| 摘要: |
| 针对无人机在跟踪地面机动目标时，目标的机动导致无法得到准确的目标状态信息的问题，以无迹卡尔曼滤波(UKF)算法为基础，结合交互多模型(IMM)算法与强跟踪滤波器的思想，提出了一种基于IMM的强跟踪无迹卡尔曼滤波(IMM-STUKF)算法的无人机对地目标状态估计算法。仿真表明，在无人机飞行速度约30 m/s、目标速度约7 m/s、角度与距离量测误差分别为1°和15 m时，相较于IMM-UKF，基于IMM-STUKF的无人机对地机动目标位置估计精度提升了约10%，速度估计误差降低了约50%。 |
| 关键词: 无人机 目标跟踪 自适应无迹卡尔曼滤波 强跟踪滤波 |
| DOI： |
|
| 基金项目:国家自然科学基金（62273178） |
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| Ground target tracking with UAV based on improved adaptive UKF |
| JIA Dacheng,ZHANG Min,XIAO Guihua |
| (College of Automation, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China) |
| Abstract: |
| Aiming at the problem that the UAV cannot obtain accurate target state information due to the maneuvering of the target when tracking the ground maneuvering target, based on the unscented Kalman filter (UKF) algorithm, by integrating the concepts of the interacting multiple model (IMM) algorithm and the strong tracking filter, a UAV ground target state estimation algorithm based on the interacting multiple model strong tracking unscented Kalman filter (IMM-STUKF) is proposed. The simulation shows that when the UAV flight speed is about 30 m/s, the target speed is about 7 m/s, and the angle and distance measurement errors are 1° and 15 m respectively, compared with IMM-UKF, the UAV ground maneuvering target position estimation accuracy based on IMM-STUKF is improved by about 10%, and the speed estimation error is reduced by about 50%. |
| Key words: UAV Target tracking Ａdaptive unscented Kalman filter Strong tracking filter |
